A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels
This addresses the issue of distorted signals in incomplete cartels for competition authorities and regulators, representing an incremental improvement.
The paper tackled the problem of detecting incomplete bid-rigging cartels by combining statistical screens with machine learning, demonstrating that their algorithm outperforms previous methods on empirical data from Switzerland.
We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging. We demonstrate that our algorithm outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.